1. Field of the Invention
The present invention relates to a restoration (reconstruction) of an image.
2. Description of the Related Art
An observed image obtained by a bright field microscope or a virtual slide including an image sensor (referred to as a “digital microscope” hereinafter) is known as an observed image obtained through a photoelectric conversion of an optical image formed by partially coherent imaging. Conventionally, no methods have been proposed for removing from the observed image a noise caused by the image sensor (which may be simply referred to as a “noise” hereinafter), or a blur caused by an optical system (which may be simply referred to as a “blur” hereinafter). It is a conventionally proposed method to restore an original image by removing the noise and blur from a deteriorated image obtained by photoelectrically converting an optical image formed by incoherent imaging.
For example, Yifei Lou, Andrea L. Bertozzi, and Stefano Soatto, “Direct Sparse Deblurring, “Journal of Mathematical Imaging and Vision,” 2011, vol. 39, p. 1-12 (“literature 1”) proposes a method for removing a noise and a blur from a deteriorated image using a set of image patches extracted from a model image and a set in which each patch is blurred by a convolution integral of a point-image intensity distribution of an optical system. Michael Elad and Michal Aharon, “Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries,” IEEE Transactions of Image Processing, 2006, vol. 15, p. 3736-3745 (“literature 2”) proposes a method for removing a noise caused by an image sensor from a deteriorated image using a set of image patches generated by machine learning from a model image.
However, the image restoration methods disclosed in the literatures 1 and 2 address incoherent imaging and cannot sufficiently deblur the observed image obtained from the optical image formed by partially coherent imaging.